50 citations · 121 across the 19 of their papers we have counts for
19 papers
Covariate-assisted graph matching
Trisha Dawn, Jesús Arroyo
Data integration is essential across diverse domains, from historical records to biomedical research, facilitating joint statistical inference. A crucial initial step in this proce…
Simultaneous estimation of connectivity and dimensionality in samples of networks
Wenlong Jiang, Chris McKennan, Jesús Arroyo +1
An overarching objective in contemporary statistical network analysis is extracting salient information from datasets consisting of multiple networks. To date, considerable attenti…
Learning Joint and Individual Structure in Network Data with Covariates
Carson James, Dongbang Yuan, Irina Gaynanova +1
Datasets consisting of a network and covariates associated with its vertices have become ubiquitous. One problem pertaining to this type of data is to identify information unique t…
Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels
Joshua Agterberg, Zachary Lubberts, Jesús Arroyo
Modern network datasets are often composed of multiple layers, either as different views, time-varying observations, or independent sample units, resulting in collections of networ…
Clustered Graph Matching for Label Recovery and Graph Classification
Zhirui Li, Jesus Arroyo, Konstantinos Pantazis +1
Given a collection of vertex-aligned networks and an additional label-shuffled network, we propose procedures for leveraging the signal in the vertex-aligned collection to recover…
Multiscale Comparative Connectomics
Vivek Gopalakrishnan, Jaewon Chung, Eric Bridgeford +8
The connectome, a map of the structural and/or functional connections in the brain, provides a complex representation of the neurobiological phenotypes on which it supervenes. This…
Overlapping community detection in networks via sparse spectral decomposition
Jesús Arroyo, Elizaveta Levina
We consider the problem of estimating overlapping community memberships in a network, where each node can belong to multiple communities. More than a few communities per node are d…
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs
Guodong Chen, Jesús Arroyo, Avanti Athreya +7
This paper considers the graph signal processing problem of anomaly detection in time series of graphs. We examine two related, complementary inference tasks: the detection of anom…
The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks
Konstantinos Pantazis, Avanti Athreya, Jesús Arroyo +3
Spectral inference on multiple networks is a rapidly-developing subfield of graph statistics. Recent work has demonstrated that joint, or simultaneous, spectral embedding of multip…
Simultaneous prediction and community detection for networks with application to neuroimaging
Jesús Arroyo, Elizaveta Levina
Community structure in networks is observed in many different domains, and unsupervised community detection has received a lot of attention in the literature. Increasingly the focu…
Graph matching between bipartite and unipartite networks: to collapse, or not to collapse, that is the question
Jesús Arroyo, Carey E. Priebe, Vince Lyzinski
Graph matching consists of aligning the vertices of two unlabeled graphs in order to maximize the shared structure across networks; when the graphs are unipartite, this is commonly…
Valid Two-Sample Graph Testing via Optimal Transport Procrustes and Multiscale Graph Correlation with Applications in Connectomics
Jaewon Chung, Bijan Varjavand, Jesus Arroyo +5
Testing whether two graphs come from the same distribution is of interest in many real world scenarios, including brain network analysis. Under the random dot product graph model,…
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks
Adam Li, Ronan Perry, Chester Huynh +6
Decision forests (Forests), in particular random forests and gradient boosting trees, have demonstrated state-of-the-art accuracy compared to other methods in many supervised learn…
Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities
Ronan Perry, Ronak Mehta, Richard Guo +6
Information-theoretic quantities, such as conditional entropy and mutual information, are critical data summaries for quantifying uncertainty. Current widely used approaches for co…
Inference for multiple heterogeneous networks with a common invariant subspace
Jesús Arroyo, Avanti Athreya, Joshua Cape +3
The development of models for multiple heterogeneous network data is of critical importance both in statistical network theory and across multiple application domains. Although sin…
Community Correlations and Testing Independence Between Binary Graphs
Cencheng Shen, Jesüs Arroyo, Junhao Xiong +1
Graph data has a unique structure that deviates from standard data assumptions, often necessitating modifications to existing methods or the development of new ones to ensure valid…
Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks
Jesús Arroyo, Daniel L. Sussman, Carey E. Priebe +1
Given a pair of graphs with the same number of vertices, the inexact graph matching problem consists in finding a correspondence between the vertices of these graphs that minimizes…
Joint Embedding of Graphs
Shangsi Wang, Jesús Arroyo, Joshua T. Vogelstein +1
Feature extraction and dimension reduction for networks is critical in a wide variety of domains. Efficiently and accurately learning features for multiple graphs has important app…
Efficient Distributed Estimation of Inverse Covariance Matrices
Jesús Arroyo, Elizabeth Hou
In distributed systems, communication is a major concern due to issues such as its vulnerability or efficiency. In this paper, we are interested in estimating sparse inverse covari…